Multi-roller registered repeat defect detection of a web process line

ABSTRACT

A manufacturing system includes rollers having synchronization marks to indicate complete rotations. Synchronization mark readers read the synchronization marks of the plurality of rollers and output roll synchronization signals. An encoder outputs a position signal indicative of a down-web distance of the web. An inspection system inspects the web and outputs anomaly data identifying positions of anomalies on the web. A synchronization unit receives the position signal from the encoder and the plurality of roll synchronization signals from the synchronization mark readers and converts the occurrence of each of the roll synchronization signals into down-web positions within a coordinate system associated with web process line. An analysis computer processes the anomaly data and the synchronization signals to identify repeated anomalies and to determine which of the rollers caused the repeated anomalies.

TECHNICAL FIELD

The invention relates to automated inspection systems, and moreparticularly, to systems for inspecting of moving webs.

BACKGROUND

Inspection systems for the analysis of moving web materials have provencritical to modern manufacturing operations. Industries as varied asmetal fabrication, paper, non-woven materials, and films rely on theseinspection systems for both product certification and online processmonitoring.

Products created on web process lines are subject to anomalies ordefects from many sources. One particular concern is web line-inducedanomalies, such as those created by continuously rotating equipmentcontacting the web in a regular, repeating pattern. Such equipment cangenerally be described as a “roll.” Typical rolls utilized within a webmanufacturing line include but are not limited to casting wheels, pullrolls, nip rolls, microreplicating rolls, web cleaning components, andidlers.

For example, the surface of a roll may be damaged (e.g., scratched) or amay have a contaminant (e.g., dirt or other particle) that induces ananomaly or defect in the moving web carried by the roll. Moreover, theroll can cause so-called “repeating anomalies” in that a new anomaly maybe imparted into the moving web with each rotation of the roll. On theresulting web product, these anomalies repeat at a distance equal to theroll's circumference in the same cross-direction or “cross-web”position. Web process lines may have hundreds of rolls, many of whichmay have similar diameters. Identifying the specific offending roll thatinduced a repeating anomaly or defect within the web can be difficultwith conventional inspection systems.

For example, commercially available web inspection systems provideidentification of repeating defects, including cross-web position anddown-web repeat distance. However, these systems typically require apriori knowledge of existing roll diameters on a given process line inorder to extract repeating defect information from the entire datastream. Moreover, in many cases there may be many idlers or other rollswithin a given web process line with circumferences that are near agiven repeat distance of a repeating anomaly, making defect-causing rollidentification difficult. As one example, a length orienter on a filmmaking line may have numerous rolls (e.g., twelve or more), all ofnominally the same eight-inch diameter. It is often difficult todetermine the unique defect-causing roll using traditional methods duein part to slight variations in diameter of each of these rolls. Inaddition, conventional systems are often unable to account for anyspatial distortion (e.g., stretching) of the web between thedefect-causing roll and the web inspection system. Further, undocumentedroll changes to a web process line can also occur. For example, asix-inch diameter roll may be replaced by a five-inch diameter roll andmay begin introducing repeat defects. Process operators usingconventional web inspection systems might not check the changed roll asthe source of anomalies or defects due to the change not beingdocumented and the assumed diameter of the roll being incorrect.

SUMMARY

In general, this application describes techniques for the automatedinspection of moving webs. More specifically, the techniques describedherein enable an automated inspection system to distinguish betweenanomalies that occur repeatedly from an identifiable source and randomanomalies for which a source may not be determinable. Certain elementsof a web manufacturing line may introduce repeated anomalies or defectsinto the web. For example, idler rollers, generally referred to hereinas “rolls,” that support the web as it traverses the system mayintroduce repeated anomalies into the web at regular intervals. Inaccordance with the techniques described herein, the automatedinspection system may identify these repeated anomalies within the weband determine the source of the anomalies. This may permit operators ofthe manufacturing line to locate the anomaly-causing element to repairor replace the offending element. Although explained for exemplarypurposes in reference to anomalies, the techniques described herein maybe readily applied to defects, and the term anomalies is used herein tomean potential or actual defects.

As described herein, the web inspection system identifies positions ofanomalies or defects within the web and then correlates those positionswith roll synchronization signals that were received during themanufacturing of the web. For example, each roll of interest for a webmanufacturing process is equipped with a synchronization mark. Duringmanufacturing of the web, the web inspection system receives a rollsynchronization signal from each of the rolls indicating that therespective roll has completed a full rotation. The web inspection systemrecords the position of each occurrence of these synchronization markswith respect to its downweb positional coordinate system. The webinspection system then correlates positional data of the rollsynchronization signals with positional data for the anomalies ordefects.

In one embodiment, the invention is directed to a method comprisingreceiving roll synchronization signals from a plurality of sensors of aweb manufacturing system, wherein each of the sensors corresponds to adifferent roller of the web manufacturing system, and wherein each ofthe roll synchronization signals indicates that the corresponding rollerhas completed a full rotation during manufacturing of a web of material.The method further comprises receiving anomaly data from a webinspection system that identifies positions of anomalies on the web. Themethod further comprises identifying a set of two or more of theanomalies as repeated, identifying which of the rollers caused therepeated anomalies by correlating the positions of the repeatedanomalies with the roll synchronization signals, and outputting anidentification of the offending one of the rollers.

In another embodiment, the invention is directed to a system comprisinga plurality of rollers in contact with a web of material, wherein two ormore of the rollers each include a synchronization mark to indicate whenthe corresponding roller has completed a full rotation. The systemincludes a plurality of synchronization mark readers that read thesynchronization marks of the plurality of rollers and output rollsynchronization signals. Each of the roll synchronization signalsindicates that the corresponding roller has completed a full rotationduring manufacturing of the web. The system also includes an encoder onat least one of the rollers that outputs a position signal indicative ofa down-web distance of the web, and an inspection system that inspectsthe web and outputs anomaly data identifying positions of anomalies onthe web. A synchronization unit receives the position signal from theencoder and the plurality of roll synchronization signals from thesynchronization mark readers, and converts the occurrence of each of theroll synchronization signals into down-web positions within a coordinatesystem associated with web process line. An analysis computer processesthe anomaly data to identify a set of two or more of the anomalies asrepeated anomalies. The analysis computer outputs an indication of whichof the rollers caused the repeated anomalies by correlating thepositions of the repeated anomalies with the down-web positions of theroll synchronization signals.

In another embodiment, the invention is directed to a computer-readablestorage medium containing software instructions. The instructions causea programmable processor of a computer to execute the softwareinstructions and perform at least some of the functions set forthherein.

The techniques described herein may provide several advantages. Forexample, the techniques may achieve significant accuracy improvementover conventional systems. For example, the techniques can be used toeasily differentiate roll sizes that differ less than 25 μm. This allowsan offending roll to be identified from a group of rolls of similardiameters, thereby enabling simpler and more robust manufacturingprocess maintenance. Further, the techniques allow repeated anomalies ordefects on a web to be detected even amidst a large number of randomdefects. In addition, the techniques allow the system to measure theexact crossweb and circumferential position of the defect producing areaof a roll, and even differentiate between multiple repeating defects atthe same crossweb position.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating a global network environment inwhich a conversion control system controls conversion of web material.

FIG. 2 is a block diagram illustrating an exemplary embodiment of aninspection system in an exemplary web manufacturing plant.

FIG. 3 is a block diagram illustrating an exemplary embodiment of a webmanufacturing system in an exemplary embodiment of a web manufacturingplant.

FIG. 4 is a block diagram illustrating an exemplary embodiment of aremote synchronization unit in greater detail.

FIG. 5 is a block diagram illustrating a system that combines rollposition data with inspection data to determine whether a roller iscausing a repeat anomaly, and if so, which of the rollers is causing therepeat anomaly.

FIG. 6 is a block diagram illustrating an example set of anomaly dataand corresponding position data from the rollers.

FIG. 7 is a block diagram illustrating an example web with severaloccurrences of random and repeated anomalies

FIG. 8 is a block diagram illustrating an example composite map formedfrom the data of FIG. 7.

FIG. 9 is a flowchart illustrating an exemplary method for identifying aroller that is causing a repeated anomaly.

FIG. 10 is a block diagram illustrating an example web that is dividedinto lanes for analysis of each lane.

FIG. 11 is a flowchart illustrating an exemplary algorithm fordetermining the presence of a repeated anomaly.

FIG. 12 is a block diagram illustrating an exemplary user interface.

DETAILED DESCRIPTION

FIG. 1 is a block diagram illustrating a global network environment 2 inwhich conversion control system 4 controls conversion of web material.More specifically, web manufacturing plants 6A-6M (web manufacturingplants 6) represent manufacturing sites that produce and ship webmaterial in the form of web rolls 7 between each other and ship finishedweb rolls 10 to converting sites 8A-8N (converting sites 8). Webmanufacturing plants 6 may be geographically distributed, and each ofthe web manufacturing plants may include one or more manufacturingprocess lines. Converting sites 8 may be part of the same entity as webmanufacturing plants 6. However, in some embodiments, converting sites 8are consumers of finished web rolls 10. Converting sites 8 may purchasefinished web rolls 10 from web manufacturing plants 6 and convertfinished web rolls 10 into individual sheets for incorporation intoproducts 12 based on grade levels. That is, the selection process ofwhich sheets should be incorporated into which of products 12 may bebased on which of the grade levels each sheet satisfies. In accordancewith the techniques described herein, converting sites 8 may alsoreceive data regarding anomalies, i.e. potential defects, in thefinished web rolls 10. Ultimately, converting sites 8 may convertfinished web rolls 10 into individual sheets which may be incorporatedinto products 12 for sale to customers 14A-14N (customers 14).

In general, web rolls 7, 10 may contain manufactured web material thatmay be any sheet-like material having a fixed dimension in one directionand either a predetermined or indeterminate length in the orthogonaldirection. Examples of web materials include, but are not limited to,metals, paper, wovens, non-wovens, glass, polymeric films, flexiblecircuits or combinations thereof. Metals may include such materials assteel or aluminum. Wovens generally include various fabrics. Non-wovensinclude materials, such as paper, filter media, or insulating material.Films include, for example, clear and opaque polymeric films includinglaminates and coated films.

In order to produce a finished web roll 10 that is ready for conversioninto individual sheets for incorporation into products 12, unfinishedweb rolls 7 may need to undergo processing from multiple process lineseither within one web manufacturing plant, for instance, webmanufacturing plant 6A, or within multiple manufacturing plants. Foreach process, a web roll is typically used as a source roll from whichthe web is fed into the manufacturing process. After each process, theweb is typically collected again into a web roll 7 and moved to adifferent product line or shipped to a different manufacturing plant,where it is then unrolled, processed, and again collected into a roll.This process is repeated until ultimately a finished web roll 10 isproduced.

An anomaly introduced into a web roll 7 by one plant, for example, webmanufacturing plant 6A, may be detectable once plant 6A has finished itsprocesses on web roll 7, but the anomaly may later become undetectableafter another web manufacturing plant, such as web manufacturing plant6B, has performed its manufacturing processes on web roll 7.

For many applications, the web materials for each of web rolls 7 mayhave numerous coatings applied at one or more production lines of one ormore web manufacturing plants 6. The coating is generally applied to anexposed surface of either a base web material, in the case of the firstmanufacturing process, or a previously applied coating in the case of asubsequent manufacturing process. Examples of coatings includeadhesives, hardcoats, low adhesion backside coatings, metalizedcoatings, neutral density coatings, electrically conductive ornonconductive coatings, or combinations thereof. A given coating may beapplied to only a portion of the web material or may fully cover theexposed surface of the web material. Further, the web materials may bepatterned or unpatterned.

During each manufacturing process for a given one of web rolls 7, one ormore inspection systems acquire anomaly information for the web. Forexample, as illustrated in FIG. 2, an inspection system for a productionline may include one or more image acquisition devices positioned inclose proximity to the continuously moving web as the web is processed,e.g., as one or more coatings are applied to the web. The imageacquisition devices scan sequential portions of the continuously movingweb to obtain digital image data. The inspection systems may analyze theimage data with one or more algorithms to produce so called “local”anomaly information. The anomaly information may include a plurality ofanomaly objects that represent distinct areas of the web and define aplurality of characteristics for the physical deviations of the web atthe corresponding area. An anomaly object may define characteristicssuch as, for example, a deviation in width of the anomalous area of theweb or a deviation in length of an anomalous area of the web. Thus thelength and width may represent a physical deviation from predefinedcharacteristics that define, for example, various grade levels. In oneexemplary embodiment, image data may be acquired and processed toidentify anomalies and to form anomaly objects as data structuresrepresenting each anomaly. Information regarding the acquisition andregistration of anomaly information is detailed in co-pending patentapplication “Multi-Unit Process Spatial Synchronization” to Floeder etal., Ser. No. 11/828,369, filed Jul. 26, 2007, assigned to the assigneeof the present application, the entire contents of which are herebyincorporated by reference.

In general, conversion control system 4 applies one or more defectdetection algorithms that may be application-specific, i.e., specific toproducts 12, to select and generate a conversion plan for each web roll10. A certain anomaly may result in a defect in one product, forinstance product 12A, whereas the anomaly may not cause a defect in adifferent product, for instance, product 12B. Each conversion planrepresents defined instructions for processing a corresponding finishedweb roll 10 for creating products 12, which may ultimately be sold tocustomers 14.

Certain elements of the process lines within web manufacturing plants 6may introduce repeated anomalies or defects into the web. For example,“rolls” that engage the web as it traverses the process line mayintroduce repeated anomalies into the web at regular intervals. Examplerolls utilized within a web process line include casting wheels, pullrolls, nip rolls, microreplicating rolls, web cleaning components, andidler rollers. In accordance with the techniques described herein,automated inspection systems, either located within manufacturing plants6 or remote, identify these repeated anomalies within the web anddetermine the source roll that induced the repeated anomalies. Thispermits operators to locate the anomaly-causing element of the systemand to repair or replace the offending element.

As described in further detail below, each of a web inspection systemidentifies positions of anomalies or defects within the web and thencorrelates those positions with roll synchronization signals that werereceived during the manufacturing of the web. For example, each roll ofinterest for a given web manufacturing process of manufacturing plants 6may be equipped with a synchronization mark. During manufacturing of theweb, the web inspection system receives a roll synchronization signalfrom each of the rolls indicating that the respective roll has completeda full rotation. The web inspection system records the occurrence ofthese synchronization marks. The web inspection system then converts theoccurrence of each of the roll synchronization signals into the spatialdomain of the inspection system for correlation with positional data forthe anomalies or defects.

The techniques described herein may provide several advantages. Forexample, the techniques may achieve significant accuracy improvementover conventional systems. For example, the techniques can be used toeasily differentiate roll sizes that differ less than 25 μm. This allowsan offending roll to be identified from a group of rolls of similardiameters. Further, the techniques allow repeated anomalies or defectson a web to be detected even amidst a large number of random defects. Inaddition, the techniques allow the system to measure the exact crossweband circumferential position of the defect producing area of a roll, andeven differentiate between multiple repeating defects on the same rollor at the same crossweb position.

Further, in some cases anomalies often appear the same to conventionalinspection systems regardless of whether the anomaly occurs on the topside of a web or on the bottom side of a web. However, it is oftendesirous to know on which side of the web defects occur because, forexample, anomalies on the one side of the web, say the bottom, may behealed by coatings on subsequent processes, but anomalies on the topside will still be visible after the final manufacturing operation.Thus, by determining the causal roll for a particular repeating anomaly,the inspection system can determine which side of the web an anomaly ison by storing data specifying the side (i.e., top or bottom) on whicheach roller is located and correlating each repeated anomaly to anindividual roller in an automated manner. Data can be output indicatingthe side of the roller causing the anomaly by displaying and indicationto a user, storing the data in a database or communicating the data toother electronic systems or devices.

The inspection system described herein may be further configured toautomatically disregard repeated anomalies on the bottom side of the webwithout alerting the operator, while immediately alerting for defects onthe top side. Alternatively, such anomalies on the bottom of the web maybe designated as at a lower alert or warning level. Thus anotherpotential advantage of the techniques described herein may beefficiently detecting and reporting of anomalies of varying degrees ofimportance.

FIG. 2 is a block diagram illustrating an exemplary embodiment of aninspection system located within a portion of a web process line inexemplary web manufacturing plant 6A of FIG. 1. In the exemplaryembodiment, a segment of a web 20 is positioned between two supportrolls 22, 24. Image acquisition devices 26A-26N (image acquisitiondevices 26) are positioned in close proximity to the continuously movingweb 20. Image acquisition devices 26 scan sequential portions of thecontinuously moving web 20 to obtain image data. Acquisition computers27 collect image data from image acquisition devices 26, and transmitthe image data to analysis computer 28 for preliminary analysis.

Image acquisition devices 26 may be conventional imaging devices thatare capable of reading a sequential portion of the moving web 20 andproviding output in the form of a digital data stream. As shown in FIG.2, imaging devices 26 may be cameras that directly provide a digitaldata stream or an analog camera with an additional analog to digitalconverter. Other sensors, such as, for example, laser scanners, may beutilized as the imaging acquisition device. A sequential portion of theweb indicates that the data is acquired by a succession of single lines.Single lines comprise an area of the continuously moving web that mapsto a single row of sensor elements or pixels. Examples of devicessuitable for acquiring the image include linescan cameras such asPiranha Models from Dalsa (Waterloo, Ontario, Canada), or Model AviivaSC2 CL from Atmel (San Jose, Calif.). Additional examples include laserscanners from Surface Inspection Systems GmbH (Munich, Germany) inconjunction with an analog to digital converter.

The image may be optionally acquired through the utilization of opticassemblies that assist in the procurement of the image. The assembliesmay be either part of a camera, or may be separate from the camera.Optic assemblies utilize reflected light, transmitted light, ortransflected light during the imaging process. Reflected light, forexample, is often suitable for the detection of defects caused by websurface deformations, such as surface scratches.

In some embodiments, fiducial mark controller 30 controls fiducial markreader 29 to collect roll and position information from web 20. Forexample, fiducial mark controller 30 may include one or more photo-opticsensors for reading bar codes or other indicia from web 20. In addition,fiducial mark controller 30 may receive position signals from one ormore high-precision encoders engaged with web 20 and/or rollers 22, 24.Based on the position signals, fiducial mark controller 30 determinesposition information for each detected fiducial mark. Fiducial markcontroller 30 communicates the roll and position information to analysiscomputer 28. Techniques for applying and using fiducial marks toidentify specific locations on a web are described in co-pending patentapplication “Apparatus and Method for the Automated Marking on Webs ofMaterial” to Floeder et al., assigned to the assignee of the presentapplication Ser. No. 10/826,995, filed Apr. 19, 2004, the entirecontents of which are hereby incorporated by reference. Althoughdiscussed with respect to fiducial marks and a fiducial mark controller30 and reader 29, fiducial marks may not be necessary in all embodimentsto effect the techniques described herein. In other embodiments, othermeans may be used to determine locations of anomalies and otherinformation on a web without departing from the techniques describedherein.

Analysis computer 28 processes image streams from acquisition computers27. Analysis computer 28 processes the digital information with one ormore initial algorithms to generate local anomaly information thatidentifies any regions of web 20 containing anomalies that mayultimately qualify as defects. For each identified anomaly, analysiscomputer 28 extracts from the image data an anomaly image that containspixel data encompassing the anomaly and possibly a surrounding portionof web 20. Analysis computer 28 may classify an anomaly into differentdefect classes if necessary. For instance, there may be unique defectclasses to distinguish between spots, scratches, and oil drips. Otherclasses may distinguish between further types of defects. In accordancewith the techniques described herein, analysis computer 28 may furtherdetermine in which of products 12 an anomaly may cause a defect.

Based on the position data produced by fiducial mark controller 30,analysis computer 28 determines the spatial position of each anomalywithin the coordinate system of the process line. That is, based on theposition data from fiducial mark controller 30, analysis computer 28determines the x, y, and possibly z position for each anomaly within thecoordinate system used by the current process line. For example, acoordinate system may be defined such that the x dimension represents adistance across web 20, a y dimension represents a distance along alength of the web, an the z dimension represents a height of the web,which may be based on the number of coatings, materials or other layerspreviously applied to the web. Moreover, an origin for the x, y, zcoordinate system may be defined at a physical location within theprocess line, and is typically associated with an initial feed placementof the web 20.

In any case, analysis computer 28 records in database 32 the spatiallocation of each anomaly with respect to the coordinate system of theprocess line, this information being referred to herein as local anomalyinformation. That is, analysis computer 28 stores the local anomalyinformation for web 20, including roll information for the web 20 andposition information for each anomaly, within database 32. Analysiscomputer 28 may also record, for each anomaly, those products ofproducts 12 for which the anomaly may cause a defect. Database 32 may beimplemented in any of a number of different forms including a datastorage file or one or more database management systems (DBMS) executingon one or more database servers. The database management systems may be,for example, a relational (RDBMS), hierarchical (HDBMS),multidimensional (MDBMS), object oriented (ODBMS or OODBMS) or objectrelational (ORDBMS) database management system. As one example, database32 is implemented as a relational database provided by SQL Server™ fromMicrosoft Corporation.

Once the process has ended, analysis computer 28 transmits the datacollected in database 32 to conversion control system 4 via network 9.Specifically, analysis computer 28 communicates the roll information aswell as the local anomaly information and respective sub-images toconversion control system 4 for subsequent, offline, detailed analysis.For example, the information may be communicated by way of databasesynchronization between database 32 and conversion control system 4. Insome embodiments, conversion control system 4 may determine thoseproducts of products 12 for which each anomaly may cause a defect,rather than analysis computer 28. Once data for the finished web roll 10has been collected in database 32, the data may be used to markanomalies on the web roll, either directly on the surface of the webwith a removable or washable mark, or on a cover sheet that may beapplied to the web before or during marking of anomalies on the web.

FIG. 3 is a block diagram illustrating further details of an exemplaryweb process line 40 in an exemplary web manufacturing plant, e.g. webmanufacturing plant 6A of FIG. 1. That is, FIG. 3 shows a typical webprocess line having various rolls. For example, although for simplicityFIG. 2 shows only idler rollers 46A-46N, process line 40 may havenumerous types of rollers including idlers, pull rolls, lengthorienters, coating rolls, and the like. In some cases, web process linemay have well over one hundred or more rolls along the entire traversalpath of web 40. Manufacturing system 40 may be part of the samemanufacturing line as the inspection system of FIG. 2, or manufacturingsystem 40 may be part of a different manufacturing line than theinspection system of FIG. 2.

Manufacturing system 40 produces web 44, typically by pulling asubstrate from lead roller 41 through manufacturing components 48A-48M(manufacturing components 48) to produce web 44 that is collected ontoweb roller 42. Accordingly, web 44 may traverse web manufacturingcomponents 48, which may manufacture web 44 in various ways. Forexample, one of manufacturing components 48, e.g. manufacturingcomponent 48A, may apply a coating to web 44.

Idler rollers 46A-46N (idler rollers 46) provide support for web 44 asweb 44 traverses web manufacturing system 40. That is, web 44 may restupon idler rollers 46 while undergoing manufacturing from manufacturingcomponents 48. Although idler rollers 46 may be necessary to properlyposition web 44, idler rollers 46 may impart anomalies or defects intoweb 44. For example, one or more of idler rollers 46 may scratch thebottom side of web 44. Although discussed with respect to idler rollers46, other types of roll, such as casting wheels, pull rolls, nip rolls,microreplicating rolls, or web cleaning components, may be present inweb manufacturing system 40, in addition to or in lieu of idler rollers46. Thus the techniques described herein are not limited to use withidler rollers, but can be applied to any roll of interest within the webprocess line. The use of idler rollers is merely exemplary for thepurpose of demonstration.

The techniques explained herein identify positions of anomalies ordefects within the web and correlate those positions with rollsynchronization signals. For example, each roll of interest for a webmanufacturing process 40 may be equipped with a respectivesynchronization mark 47A-47N. Further, synchronization mark readers50A-50N (synchronization mark readers 50) are associated with each oneof the rolls of interest (each one of idler rollers 46 in this example)for sensing the respective synchronization mark. Each of synchronizationmark readers 50 may detect when the corresponding one of idler rollers46 has made a full rotation and then emit a roll synchronization signalin the form of a trigger pulse, which remote synchronization unit 54detects. That is, each of synchronization mark readers 50 may output ashort pulse upon a complete rotation of the respective one of rollers46, and the leading edge of each short pulse may indicate the completerotation has been detected. In one embodiment, each of synchronizationmark readers 50 may be a photo-optic sensor. For example, readers 50 maybe from the D10 Family of sensors from Banner Engineering Corp. Ingeneral, readers 50 detect corresponding synchronization marks 47 as themarks rotate past the reader. In the example embodiment, synchronizationmarks 47 may be a target such as a retro-reflecting material or amachined section of the roll. Upon detecting reference pointsynchronization marks 47 on a corresponding one of rollers 46, the oneof readers 50 outputs the synchronization mark signal. Therefore, eachof readers 50 outputs a discrete signal for each rotation of thecorresponding one of rollers 46.

To aid converting the roll synchronization signals into a spatial domainof a coordinate system associated with web process line 40, a rotationalencoder is affixed to one or more rolls along the process line. In thisexample, rotational encoder 52 is affixed to web roller 41. In otherembodiments, an encoder may be used with one or more of rollers 46 inlieu of, or in addition to, encoder 52. Encoder 52, in one embodiment,may be a sine encoder based position sensor. Other embodiments mayutilize other types of position sensors or encoders. In general, encoder52 outputs an electrical pulse train that is directly synchronized tothe physical movement of web roller 41. For example, encoder 52 may emita series of pulses for each rotation of web roller 41. In oneembodiment, for example, encoder 52 may emit four million pulses perrotation, thus providing a high degree of positional accuracy.

Remote synchronization unit 54 receives the positional pulses fromencoder 52 and the roll synchronization signals from synchronizationmark readers 50 and generates a logical map that identifies varioussections of web 44 that align with each of idler rollers 46. Forexample, for each of the rollers, remote synchronization unit 54 dividesthe spatial domain of the web into a series of sections, each of thesections being as long as the circumference of respective roller. Eachweb section corresponding to idler roller 46A, for example, is 18.85inches, i.e. 6.00 inches*π. Each web section corresponding to idlerroller 46B is 18.91 inches, and a web section that corresponds to idlerroller 46C is 18.79 inches. In this way, remote synchronization unit 54uses the positional data from encoder 52 as well as roll synchronizationsignals from synchronization mark readers 50 to convert the rollsynchronization signals into the spatial domain of the coordinate systemfor process 40 for determining web sections within the spatial domainfor each or the rollers of interest. As a result, remote synchronizationunit 54 need not require a priori data regarding the exact diameter ofeach of rollers 46 in order to determine the web sections and ultimatedetect repeated defects.

In some cases, some or all of the rolls of interest may haveapproximately the same diameter. For example, a subset or all of idlerrollers 46 may have approximately the same diameter of six inches.However, this subset of idler rollers 46 typically does not have exactlythe same diameter due to manufacturing variability. For example, thediameter of idler roller 46A may be 6.01 inches, the diameter of idlerroller 46B may be 6.02 inches, and the diameter of idler roller 46C maybe 5.98 inches. The techniques described leverages averaging captured bycalculating variations in the relative offset between a repeated defectand the corresponding roll synchronization signal for a given roller.This provides precise accuracy that allows for repeat defect detectioneven in manufacturing lines having substantially similar sized rollersbut for manufacturing variability in the rollers themselves.

In order to associate an anomaly with one of idler rollers 46, aninspection system may first collect data regarding web 44. Using thepulses from encoder 52 and the roll synchronization signals fromsynchronization mark readers 50 that has been collected and correlatedby remote synchronization unit 54, the inspection system analyzes theanomaly data for the identified web sections for each of the rollers.The inspection system may average the results of the data over manyinstances of these web sections. For example, in one embodiment, theinspection system may collect 100 instances of web segment data for agiven roller. The inspection system then analyzes the data to attempt todistinguish between repeated anomalies and random anomalies. Theinspection system may determine that an anomaly is a repeated anomaly,caused by one of idler rollers 46 for example, if an anomaly occurs in amajority of the instances of analyzed web sections for a given roller ator relatively near the same position in those instances in which theanomaly occurs. For example, if idler roller 46A causes an anomaly inweb 44, the anomaly will probably be repeated, and the instances of therepeated anomaly should occur approximately 18.85 inches apart, given adiameter of roller 46A of 6.00 inches.

In some arrangements, at least some of the anomalies imparted to web 44by idler rollers 46 may be healed, i.e. corrected, by the time web 44 isready to be converted into sheets. In other words, although idlerrollers 46 may impart an anomaly into web 44, the anomaly may not causea defect because the anomaly may be corrected through othermanufacturing processes before web 44 is ready to be converted. Forexample, anomalies imparted to web 44 by idler rollers 46 will be on thebottom side of web 44. Anomalies occurring on the top of web 44 may notbe healed or corrected in web 44. That is, anomalies occurring on thetop surface of web 44 may cause defects in products 12 if a web segmentor individual sheet containing such anomalies is converted into one ofproducts 12. In accordance with the techniques described herein, aninspection system may be able to determine whether an anomaly occurredon the top side or bottom side of web 44. Moreover, the inspectionsystem may be able to trace the source of anomalies occurring on the topside to a particular one of idler rollers 46, for example, idler roller46A. Accordingly, an operator of manufacturing system 40 may locate theportion of idler roller 46A that caused the anomalies and repair idlerroller 46A.

FIG. 4 is a block diagram illustrating an exemplary embodiment of remotesynchronization unit 54 in greater detail. As illustrated in FIG. 3,remote synchronization unit 54 may be electrically coupled to encoder 52and synchronization mark readers 50 to receive signals therefrom.

In general, example remote synchronization unit 54 senses the occurrenceof each roll synchronization signal (illustrated in FIG. 4 as “OnceAround” signals A, B-N) is received and converts the signals to aspatial domain relative to the position data from encoder 52. Moreover,synchronization unit 54 outputs positional data identifying the positionof synchronization signals that corresponds to one rotation of thatrespective roller.

In the example embodiment, remote synchronization unit 54 includescounters 56A-56N (“counters 56”) and registers 58A-58N (“registers 58”).Each of synchronization mark readers 50 is associated with one ofcounters 56, which is in turn associated with one of registers 58. Thepulse signal from encoder 52 is used as a global increment drivingcounters 56. That is, as encoder 52 detects web movement, encoder 52sends a series of pulses that are used to simultaneously increment eachof counters 56. In the exemplary embodiment of FIG. 4, roller 46A mayinclude a series of holes around the outer edge of the roller throughwhich a light may shine. Each time encoder 52 detects light through oneof the holes, encoder 52 may transmit a signal to each of counters 56.Counters 56, in turn, may receive the pulse train of the encoder signalin parallel and concurrently increment their respective counters.

The roll synchronization signals from each of the rollers are used astriggers for recording the value within the rollers' respectivecounters. Specifically, during a full rotation of any of rollers 46, thecorresponding synchronization mark 47 of that roller will pass theassociated synchronization mark reader 50. For example, for eachrotation of roller 46A, synchronization mark reader 50A will detectsynchronization mark 47A. Upon detecting mark 47A, synchronization markreader 50A outputs a roll synchronization signal to remotesynchronization unit 54 in the form of a short pulse. In response tothis pulse, remote synchronization unit 54 latches the current value ofthe corresponding counter, in this case, counter 56A, into thecorresponding data register, register 58A.

Controller 60 polls each of registers 58 at a high rate or is interruptdriven to retrieve the most recent counter data. Accordingly, thepolling cycle of controller 60 is faster than the rotations of all ofrollers 46. If, upon polling one of registers 58, e.g. register 58A, thecounter data is the same as the previous poll, controller 60 may ignorethe current counter data. However, if the counter data has changed,controller 60 may retrieve the counter data and transmit the counterdata, along with the roller number, to analysis computer 59 (FIG. 5).That is, upon detecting a change to one data register 58, controller 60of synchronization unit 54 outputs roll position data in the form acurrent encoder pulse count. Analysis computer 59 can harmonize thisroll positional data for each of the rollers with inspection data, asdescribed with respect to FIGS. 5 and 6, in order to determine whetherany anomalies are repeat anomalies caused by one of rollers 46, as wellas to determine which of rollers 46 is causing the repeat anomaly.Analysis computer 59 may output data to a display to indicate which ofrollers 46 caused each set of repeated anomalies. For example, analysiscomputer 59 may output a graphical representation of portions of the webas well as an indication of the repeated anomalies and the identifiedroller that caused the repeated anomalies. In addition, analysiscomputer 59 may output and store data in a database (e.g., database 32)associating the repeated anomalies with the identified roller causingthe repeated anomaly.

FIG. 5 is a block diagram illustrating a system 61 in which an analysiscomputer 59 combines the roll position data from one or more remotesynchronization units (e.g., remote synchronization unit 54 of FIGS. 3and 4) with inspection data to determine whether one of rollers ofinterest (e.g., any of rollers 46) is causing a repeat anomaly, and ifso, which of the rollers is causing the repeat anomaly. Analysiscomputer 59 may be coupled to one or more web inspections components, asshown by way of example with respect to analysis computer 28,acquisition computers 27 and image acquisition devices 26 of FIG. 2. Theuse of inspection systems to inspect webs for the presence of anomaliesis described in greater detail in co-pending applications “Multi-UnitProcess Spatial Synchronization” to Floeder et al., Ser. No. 11/828,369,filed Jul. 26, 2007, assigned to the assignee of the presentapplication, and “Apparatus and Method for the Automated Marking ofDefects on Webs of Material” to Floeder et al., Ser. No. 10/826,995,filed Apr. 19, 2004, assigned to the assignee of the presentapplication, the entire contents of which are hereby incorporated byreference.

In one embodiment, analysis computer 59 may be a server-class computer.In other embodiments, analysis computer 59 may be a distributedcomputing system or other computing system capable of handling the highamounts of data required for processing the inspection and positioninformation.

As described above, controller 60 of remote synchronization unit 54transmits roll position data upon detecting a rotation of one of therollers 46, and the roll position data may be in the form of a rolleridentification (i.e., a trigger number) and the current encoder positionrecorded representing the downweb position (DW position) for a givencomplete rotation of that roller. In some embodiments, encoder 52 maytransmit positional pulses both to remote synchronization unit 54 and tothe inspection systems to allow correlation within the spatial domain ofthe web segments of the rolls and detected anomalies. In otherembodiments, two distinct encoders may be used to provide positionalreference information that is reconciled by analysis computer 59. Instill other embodiments, a different means of tracking distance down theweb, such as fiducial marks, may be employed by the inspection system.Techniques for using fiducial marks with a web are discussed inco-pending patent application “Fiducial Marking for Multi-Unit ProcessSpatial Synchronization,” Ser. No. 11/828,376, to Floeder et al.,assigned to the assignee of the present application, filed Jul. 26,2007, the entire contents of which are hereby incorporated by reference.

In any case, analysis computer 59 correlates the roll position data fromremote synchronization unit 54 with positional data of anomalies on theweb as determined by the inspection system. Video or other image datamay be passed from the inspection sensors to acquisition computers62A-62M (“acquisition computers 62”). These computers represent softwareand/or hardware capable of acquiring and processing inspection data fordetection of various types of anomalies on the web, e.g. scratches,spots, drips, spills, or other types of anomalies. For example,acquisition computers 62 may be software modules executing on analysiscomputer 59 or analysis computer 29 of FIG. 2. Alternatively,acquisition computers 62 may be discrete units separate from theanalysis computer. In either case, when one of acquisition computers 62detects an anomaly, for example, when acquisition computers 62A detectsan anomaly, sensor 62A outputs anomaly data specifying the type ofanomaly, the cross-web position of the anomaly, and the down-webposition of the anomaly.

Analysis computer 59 processes the anomaly data and the roll positiondata to determine whether any anomalies repeatedly occur atsubstantially the same cross-web position and substantially the samedownweb offset within multiple web segments for the same roller. Forexample, if one of rollers 46 causes a repeated anomaly, the repeatedanomaly occurs at substantially the same cross-web position and willrepeat with a spacing of the circumference of the corresponding roller,i.e. the circumference of the roller causing the repeated anomaly. Inthis manner, analysis computer 59 may determine that repeated anomaliesare occurring. Moreover, correlating the downweb positions of therepeated anomalies with the downweb positions of the web segments forthe different rollers, analysis computer 59 is able to identify which ofrollers 46 is causing each of the repeated anomalies.

FIG. 6 is a block diagram illustrating an example set of anomaly data 63and corresponding roll position data 65. Before processing by analysiscomputer 59, all anomalies may appear to be the same, that is, randomand repeated anomalies are visually indistinguishable. However, afteranalysis, analysis computer 59 distinguishes repeated anomalies fromrandom anomalies 74 from repeated anomalies 64, 66, 70, and 72.

Encoder 52 and synchronization mark readers 50 create a series of pulsesthat graphically depict the position of each of rollers 46 over timealong the downweb length of web 67. Encoder pulses from encoder 52 andsynchronization pulses from synchronization mark readers 50 arerepresented signals 76 and graphs 78A-78N (“graphs 78”), respectively.Based on the data, roll position data, analysis computer 59 determinesthe number of encoder pulses from encoder 52 that occur betweensynchronization pulses from one of synchronization mark readers 50. Inthe example of FIG. 6, roller 46A has 11 encoder pulses per rotation,roller 46C has 19 encoder pulses per rotation, and both rollers 46B and46D have 9 encoder pulses per rotation.

Analysis computer 59 determines that anomalies 64A-64D (“anomalies 64”)occur at a similar cross-web position. Analysis computer furtherdetermines that one of anomalies 64 occurs one encoder pulse after eachsynchronization pulse from roller 46C. That is, in this example thedownweb positions of the anomalies are constant offsets from the startof new web segments for roller 46C. Therefore, analysis computer 59determines that anomalies 64 are repeated anomalies and that roller 46Cis the cause. An operator may then inspect roller 46C at the cross-webposition of repeated anomalies 64 and either repair or replace roller46C.

Similarly, a set of anomalies 66A-66D (“anomalies 66”) all occur at thesame cross-web position. However, there are missing anomalies 68A and68B that were expected to occur. It is possible that the offendingroller did not cause an anomaly, or that the inspection system failed todetect an anomaly at one or both of positions 68A and 68B. In eithercase, however, analysis computer 59 may still determine the presence ofa repeated anomaly. This is because, even with missing anomalies 68A and68B, analysis computer 59 determines the presence of a repeated anomalywhen a majority of the new web segments for a roller contain anomaliesin the same cross-web position and the substantially the same distancefrom the synchronization pulses, i.e., the start of a new web segmentfor that roller. In this case, each of repeated anomalies 66 occur 7encoder pulses after a majority of synchronization pulses of signal 78A.Therefore, analysis computer 59 may determine that roller 46A is causinga repeated anomaly.

The techniques described herein may even be used to detect repeatedanomalies 70A-70G (“repeated anomalies 70”) and repeated anomalies72A-72G (“repeated anomalies 72”) and to distinguish repeated anomalies70 from repeated anomalies 72. Repeated anomalies 70 and repeatedanomalies 72 each occur at the same cross-web position. Repeatedanomalies 70 each occur 1 encoder pulse after a synchronization pulse ofgraph 78B and 4 encoder pulses after a synchronization pulse of graph78D. Repeated anomalies 72 each occur 7 encoder pulses after asynchronization pulse of graph 78B and 1 encoder pulse after asynchronization pulse of graph 78D. Although it would appear that eitherof rollers 46B or 46D could be causing either of these repeated defects,analysis computer 59 may still determine which of repeated defects 70and 72 are caused by rollers 46B and 46D, because the diameters ofrollers 46B and 46D likely differ by some detectable amount. For ease ofvisualization and readability, a small number of encoder pulses is shownin the example of FIG. 6. However, in many embodiments, far more encoderpulses are used between synchronization pulses. In one embodiment, forexample, as many as four million encoder pulses may occur betweensynchronization pulses. At this resolution, it is possible to detecteven extremely small differences in position over time. Therefore, iftwo distinct rollers with nominally the same diameter are causing twosets of repeated defects in the same cross-web position, when related tothe synchronization pulses for one of the two rollers, one set ofanomalies will appear stationary while the other set will appear to besliding. This is illustrated conceptually in FIGS. 7 and 8.

FIG. 7 is a block diagram illustrating an example web 80 with severaloccurrences of random and repeated anomalies. Web 80 may correspond to,for example, web 44. In this example, web 80 may have traversed threeidler rollers, e.g. idler rollers 46A, 46B, and 46C. Idler rollers 46A,46B, and 46C may have the same nominal diameter of six inches, but theactual diameters may differ slightly for each of the rollers.Synchronization marks corresponding to idler rollers 46 are used tologically determine web segments for a given roller. In the example ofFIG. 7, dashed lines are used to indicate the divisions between websegments 82A-82D (web segments 82), that is, the dashed lines representsynchronization pulses from one of the synchronization mark readers 50for one of the rollers 46. Each dashed line occurs after a constantdistance 102, which corresponds to one of the circumferences of idlerrollers 46, i.e. the distance between synchronization pulses. In thiscase, for example, distance 102 may be 18.85 inches.

Because of this segmentation, it is possible to determine whether, forexample, idler roller 46A is causing any of the anomalies on web 44. Websegment 82A includes anomalies 84A, 86A, 88A, 90, and 92. Web segment82B includes anomalies 84B, 86B, 88B, and 94. Web segment 82C includesanomalies 84C, 86C, 88C, 96, and 98. Web segment 82D includes anomalies84D, 86D, 88D, and 80. To determine whether any of these anomalies is arepeated anomaly caused by idler roller 46A, the analysis computerdetermines the distance between each anomaly and each synchronizationpulse, i.e. the beginning of each web segment as represented by eachdashed line. Although only four web segments 82 are shown in FIG. 7 forpurposes of illustration, many more segments may be used for analysis.In one embodiment, for example, the analysis computer may analyze ahundred web segments before making decisions regarding repeatedanomalies.

The analysis computer repeats this analysis for each of the rollers.That is, the analysis computer segments the web in a similar manner foreach synchronization pulse, allowing the computer to identify thespecific source of the repeated anomalies.

FIG. 8 is a block diagram illustrating an example composite map 110formed from the data of FIG. 7 as segmented for a single roller. Thatis, composite map 110 has a total downweb length 102 (18.85 inches inthis example), where each of the web segments are overlaid. As a result,composite map 110 includes the anomalies from each of the web segments82 of web 80, and those anomalies have been spatially registered to thestart of the web segment as defined by the synchronization pulses forthat particular roller.

In composite map 110, anomalies 84, 86, and 88 each appear to berepeated anomalies. However, composite map 110 shows that repeatedanomalies 84 shift in the down-web direction during different websegments. That is, anomalies 84 and 88 may be repeated anomalies, butthey are not repeated at the interval of the circumference of idlerroller 46A. Analysis computer 59 may determine this by determining thatthe distance from the synchronization pulse for this specific roller toeach of anomalies 84 and 88 exceeds a threshold difference for eachinstance of anomalies 84 and 88.

In contrast, anomalies 86 are repeated anomalies and caused by theroller for which the data has been segmented because, as shown bycomposite map 110, they are spaced at substantially the same interval ofthe circumference of idler roller 46A. That is, for each instance ofanomalies 86, the distance between the synchronization pulse and theinstance of anomalies 86 is within a tolerance distance. The tolerancedistance may, for example, be ±2 pulses depending on the positionalresolution of the encoder. Therefore, the inspection system maydetermine that anomalies 86 are repeated anomalies caused by idlerroller 46A. For example, anomalies 86 may be scratches on the bottomside of web 80 caused by a rough spot on idler roller 46A. Using thisdetermination, an operator may attempt to repair idler roller 46A atthis position to prevent idler roller 46A from causing more anomalies.

In some embodiments, the inspection system may be reprogrammed todisregard anomalies occurring at a similar position in later websegments, as these anomalies will very likely not actually cause adefect once web 80 is finally converted into products. That is, nearlyall of anomalies caused by idlers or other rollers known to be locatedon a bottom side of the web may be cured at some point during themanufacturing of web 80.

The random anomalies 90, 92, 94, 96, 98, and 80 of web 80, however,probably occurred on the top side of web 80, and anomalies 90, 92, 94,96, 98, and 80 will probably not be cured during the remainder of themanufacturing of web 80. Therefore, the inspection system may mark thepositions of these anomalies in a database, such as database 32 of FIG.2, or on the surface of the web, and the system may also note that theseanomalies will likely cause defects once web 80 is converted intoproducts.

FIG. 9 is a flowchart illustrating an exemplary method for identifying aroller that is causing a repeated anomaly. The method is discussed withrespect to analysis computer 59, although the method is not limited toperformance by a single computer. Initially, analysis computer 59receives anomaly data from sensors 62 (120). As discussed above, sensors62 represent software and/or hardware capable of acquiring andprocessing inspection data for detection of various types of anomalieson the web, e.g. scratches, spots, drips, spills, or other types ofanomalies. The anomaly data output by sensors 62 includes both thecrossweb and downweb positions of the anomalies on a web, such as web 44of FIG. 3. The anomaly data may further include anomaly type informationthat may identify what type of anomaly the identified anomaly is, suchas a hole, a pit, a scratch, a discoloration, or other type of anomaly.

Analysis computer 59 also receives roller data (122). The roller datamay include identifications of each roller, as well as datacharacterizing the occurrences of complete rotations of each roller. Forexample, the roller data may identify roller 46A using a uniqueidentifier or label, which may be assigned by a user, and includetrigger numbers (e.g., a sequence number) and a down web position foreach instance when synchronization mark reader 50A read synchronizationmark 47A.

Next, analysis computer 59 correlates the received anomaly data with thereceived roller data (124). Initially, analysis computer 59 processesthe roller data to logically partition the web into a series ofsegments, and may repartition the web in a similar manner for eachroller of interest. That is, for each roller of interest, the length ofeach segment in the series is defined by the distance between twosequential trigger signals from its corresponding one of synchronizationmark readers 50. As a result, the length of each of the segments forthat partitioning is substantially equal to the circumference of thecorresponding one of rollers 46. For example, analysis computer 59 maylogically partition the web into a set of segments for roller 46A. Thedown web distance between signals from synchronization mark reader 50Awith respect to a coordinate system of the process line will,accordingly, be equal to the circumference of roller 46A. As describedin greater detail below, the anomaly data for each of these logicalsegments for a given roll of interest may be analyzed to determine thepresence of anomalies in substantially common positions within thesegments, that is, anomalies that occur at a common cross-web locationand a common down-web distance from the beginning of all or a thresholdnumber of the logical segment. This threshold, in one embodiment, may bea majority of the segments. In some embodiments, the width of eachsegment may be the width of the web. In other embodiments, such as thatdescribed with respect to FIG. 10, the web may be subdivided into lanesin the cross-web direction, such that the width of the segments aredefined by the width of the corresponding lane.

Based on the logical partitioning of the web for each of the rollers ofinterest, analysis computer 59 identifies positions of anomalies on eachof the segments. In this manner, analysis computer 59 determines thepositions of each anomaly relative to each rotation of each roller.Analysis computer 59 then analyzes the anomaly data (126) to determinethe presence of repeating anomalies (128). Analysis computer 59determines, for each roller, whether an anomaly is occurring insubstantially the same position relative to the rotation of the roller.That is, analysis computer 59 determines whether any of the anomalies isin substantially the same position on the logical segments for any ofthe rollers. For example, analysis computer 59 may determine that ananomaly occurs 16 inches cross-web and five inches down-web for all orfor a threshold number of the segments for a given partitioning.

By determining the presence of a repeating anomaly, analysis computer 59may then identify the anomaly-causing roller of rollers 46 (130). Forexample, analysis computer 59 may determine that roller 46A is causing arepeated anomaly because the anomaly at issue occurs substantially thesame cross web and down web location after each rotation of roller 46A.In response, analysis computer 59 may output the identity of theanomaly-causing roller (132). For example, analysis computer 59 mayoutput the identity to a computer screen. Other means of identifying theanomaly-causing roller may also be used, such as, for example, causing alight on or near the offending roller to illuminate. As another example,analysis computer could illuminate a light-emitting diode (LED) that isassociated with the offending roller, wherein each roller is associatedwith an LED and the LEDs may be positioned on a board to provide acentral viewing location to an operator. Additionally, analysis computer59 may further output the position of the anomaly to assist an operatorin repair of the offending roller. The operator may determine theposition of the synchronization mark on the roller and, using theposition of the anomaly, inspect the roller at the position of therepeated anomaly to determine whether the repeated anomaly-causingelement is repairable.

FIG. 10 is a block diagram illustrating an example web 152 that islogically divided into lanes 154A-154K (“lanes 154”) for analysis ofeach lane. In one embodiment, in order to determine whether there is anoccurrence of a repeating anomaly, web 152, which may represent web 67,for example, may be divided into lanes, such as lanes 154. An analysissystem, such as the inspection system of FIG. 2, may inspect each oflanes 154 individually. Because repeated anomalies will occur in thesame general cross-web position, division of web 152 into lanes 154 mayincrease the efficiency of data gathering. That is, each lane may beinspected individually without regard for anomalies occurring in theother lanes.

In the example embodiment of FIG. 10, web 152 has been divided intolanes 154A-154K. The number of lanes depicted is merely exemplary, andthe choice of the number of lanes may be made as a result of the size ofweb 152, the number of inspection devices available, or other factors.Lanes 154A, 154C, 154E, 154I, and 154K are demarcated by dashed lines,whereas lanes 154B, 154D, 154F, 154H, and 154J are demarcated bydashed-dotted lines. In the example of FIG. 10, adjacent lanes overlapslightly so that an occurrence of a repeating anomaly along the edge ofa lane will be detected as well as repeating anomalies occurring in thecenter of the lane. Lane widths as small as 5 mm have proven useful.

Image acquisition devices, such as image acquisition devices 26 of FIG.2, may inspect web 152 at lanes 154. One of image acquisition devices 26may inspect each of lanes 154. Analysis computers 27 may determinewhether corresponding image acquisition devices 26 have detected ananomaly, as described with respect to FIG. 2. Moreover, analysiscomputer 59 of FIG. 5 may determine whether a repeated anomaly isoccurring in any of lanes 154. In one embodiment, analysis computer 59may use the algorithm described with respect to FIG. 11 to determine thepresence of a repeating anomaly in one of lanes 154.

Because a repeating anomaly may occur in an overlap of lanes, such as inthe overlapping region between lanes 154A and 154B, such an anomaly maybe detected twice. The inspection system may use various factors toreconcile such a duplicate detection. For example, the inspection systemmay compare the cross-web position of the repeated anomalies, as well asthe down-web position of each instance of the anomalies and the repeatinterval between instances. When two repeated anomalies are discoveredwith the same cross-web position and instances of the anomaliesoccurring at the same down-web positions at the same interval, thesystem may discard one of the repeating anomalies so as not to triggertwo alerts for the same repeating anomaly.

FIG. 11 is a flowchart illustrating another exemplary algorithm fordetermining the presence of a repeated anomaly. The method of FIG. 11may be used to effect the result of step 128 of FIG. 9 in one exemplaryembodiment. In one embodiment, the method of FIG. 11 may be separatelyapplied to data gathered from each of lanes 154 of FIG. 10, such as, forexample, lane 154A.

Initially, analysis computer 59, in an example embodiment, determines astarting point A, which may be a first detected anomaly (160). Asdiscussed above, a repeating anomaly is an anomaly that is caused by anelement of a web production or manufacturing system, such as an idlerroller. Therefore, there is a certain distance, herein referred to as“R_(min)” which is the minimum possible repeating distance for arepeated anomaly. For example, in the case of repeated anomalies causedby one or more of a plurality of idler rollers used within a webprocess, R_(min) is the circumference of the smallest idler roller ofinterest. Accordingly, analysis computer 59 may search for a point B inlane 154A such that the cross-web position of points A and B are thesame and the down-web distance between points A and B is at leastR_(min) (162).

Analysis computer 59 may then determine whether a point C exists in lane154A such that the cross-web position of point C is the same as that ofA and B and such that the down-web distance between points B and C is acertain multiple of the distance between points A and B (164). Arepeated anomaly may not repeat in every expected instance. Severalinstances of the repeated anomaly may be skipped, as discussed withrespect to FIG. 6. In determining whether point C is an instance of arepeated anomaly, therefore, the exemplary embodiment determines whetherthe distance between points B and C is a multiple of the distancebetween points A and B. In one exemplary embodiment, the multiple may beone of 1, ½, ⅓, 2, or 3. That is, based on the detection capability fora given application, an expert user can predefine the number of integermultiples to be used for identifying sparsely repeating defects. Forexample, for a given system with very high detection capability, theinteger multiple may be 1 while a second system with lower detectioncapability may use a multiple of 5. The first examines only a singledownweb distance from a given anomaly while the second examinesmultiples of 1, 2, 3, 4, 5 and ½, ⅓, ¼, and ⅕. Note, the computationalcomplexity increases with increased multiples. In practice multiples of3 may be generally sufficient.

If no point C can be found at a distance from point B that is, forexample, 1, ½, ⅓, 2, or 3 times the distance between points A and B(“NO” branch of 166), analysis computer 59 may obtain a new startingpoint anomaly A (168) and attempt to determine whether the new startingpoint is part of a repeating anomaly. If analysis computer 59 does findsuch a point C, however, (“YES” branch of 166), analysis computer 59 maythen search for a point D where the distance between points C and D is amultiple of the distance between points A and B (170). In oneembodiment, the same set of potential multiples may be used as in step164, e.g. 1, ½, ⅓, 2, and 3. Point D may be used to confirm that pointsA, B, and C are indeed part of a sequence of repeating anomalies.

If no point D is found (“NO” branch of 172), analysis computer 59 mayagain restart the process of selecting a new starting anomaly point A(168). No point D may be found if, for example, anomalies at points A,B, and C were not part of a repeating anomaly and the distances betweenpoints A and B and between points B and C were merely coincidental.However, if analysis computer 59 does find a point D (“YES” branch of172), it is quite likely that points A, B, C, and D make up a column ofrepeated anomalies. Therefore, analysis computer 59 may determine arepeat distance as the minimum of the distances between points A and B,points B and C, and points C and D (174). Analysis computer 59 may thenexpect to discover anomalies repeated at the determined repeat distancefrom point D at the cross-web position of points A, B, C, and D.Analysis computer 59 may analyze each of lanes 154 to discover repeatedanomalies in a similar manner.

After having determined a repeated anomaly, analysis computer 59 maydetermine the source roller of the repeated anomaly, per the method ofFIG. 9. For example, analysis computer 59 may calculate an offsetbetween one instance of a repeated anomaly and point A, i.e. the firstrecognized instance of the repeated anomaly. Analysis computer 59 maythen use this offset to project an estimated position of the one ofsynchronization marks 47 corresponding to the one of rollers 46 underanalysis. Analysis computer 59 may then determine whether thesynchronization mark was recorded within a certain error tolerance ofthe estimated position. If the synchronization mark was recorded at theestimated position, or within a predetermined tolerance level of theestimated position, then the roller corresponding to the analyzedsynchronization mark is the offending roller. However, if thesynchronization mark was not recorded at the estimated position orwithin the tolerance level, the roller corresponding to thesynchronization mark is not the roller causing the repeated anomaly.

The error tolerance applied by analysis computer 59 may be a function ofthe expected number of complete rotations separating the anomalies. Forexample, for two nearly identical rollers having diameters of 20.000 cmand 20.0001 cm, the down web distance separating two repeat intervalsfor the rollers will be approximately 62.800 cm and 62.803 cm, which maybe too small to measure. However, after 50 expected complete rotationsfor the rollers, the down web positions of the end of the web segmentwill be 3140 cm and 3140.15 cm, yielding a positional difference of 0.15cm, which is a measurable error tolerance applied by analysis computer59.

As an example, the first position, i.e. the position of point A, for arepeated anomaly series may have been recorded at 0.4924 m and then^(th) instance of a repeated anomaly may have occurred at a down-webdistance of 79.5302 m. The offset would then be 79.1008 m (79.5302m−0.4924 m). The first synchronization mark of roller 46A (FIG. 3) mayhave been read by synchronization mark reader 50A at position 0.0012 m.Therefore, if roller 46A is causing the repeated anomalies, the positionrecorded for synchronization mark 47A nearest the n^(th) anomaly in theseries should be relatively near 79.1020 m (0.0012 m+79.1008 m). If thesynchronization mark nearest the analyzed anomaly was actually recordedat 78.7508 m, the error would be 0.3512 m, which is significant enoughto determine that roller 46A is not the roller causing the repeatedanomaly. However, the first recorded synchronization mark for roller 46Bmay have been at 0.0001 m. Therefore, the position recorded forsynchronization mark 47B may be expected at 79.1009 m (79.1008 m+0.0001m). If the actual recorded position of synchronization mark 47B was79.1018 m, then the error would only be 0.0009 m, which would indicatethat roller 46B is causing the repeated anomaly.

Although discussed with respect to the use of lanes 154, the methoddescribed above is not limited to the use of lanes 154. For example, themethod may be applied to a full web 152 that has not been divided intolanes 154. In another example embodiment, multiple analysis computersmay be used, one analysis computer for each lane, rather than a singleanalysis computer 59. For example, acquisition computers 27 of FIG. 2may be programmed to effect the method of FIG. 11 for correspondinglanes 154. Each of acquisition computers 27 may then upload thediscovered repeated anomalies to analysis computer 59 forreconciliation. The method described above may be encoded into acomputer-readable storage medium in the form of software instructionsthat cause a processor of a computer to perform the steps of the method.

FIG. 12 is a block diagram illustrating an exemplary user interface 180.User interface 180 may be implemented as a graphical user interface(“GUI”) depicting a variety of information. For example, user interface180 may include data output area 182. Data output area 182 may displayvarious raw and/or summarized data for a user interacting with thesystem, for example, through analysis computer 59.

In the exemplary embodiment of FIG. 12, data output area 182 includes a“repeat” area 183A that displays information on detected repeatedanomalies, as well as a “slip” area 183B that displays informationregarding detected roll slip. Repeat area 183A includes roll identifiercolumn 186, priority column 188, action description column 190, and mapcolumn 192. Entries in roll identifier column 186 identify the roll towhich the entries in the row correspond. For example, the first entry inroll identifier column 186 is “1”, indicating that the row includesinformation on the roll identified as “1”.

Entries in priority column 188 indicate to a user how important orsignificant the detected repeated anomaly is. In the example of FIG. 12,the priority is illustrated as “high”, “medium”, or “low”. Otherembodiments may use different priority levels and indicators, such as“green”, “yellow”, or “red”, or a numeric scale, e.g. 1-10.

Entries in action description column 190 indicate to a user thesuggested or required action that the user should take. For example, thefirst entry in description column 190 is “pull roll #3”. A user viewingthis display should replace the roller identified with the number “3”with a new roller. Moreover, given a priority of “high” in prioritycolumn 188, a user should replace roll “3” as soon as possible.

Map column 192 allows a user to select a roller and view the compositemap on map screen 184. For example, a user may use a mouse connected toanalysis computer 59 to direct a pointer to one of the cells in column192 and press a button to select the corresponding roller. In theexample of FIG. 12, a user has selected roll “4”. Accordingly, analysiscomputer 59 has displayed the composite map corresponding to roll “4” inmap window 184. The composite map in window 184 may be similar tocomposite map 110 of FIG. 8. Analysis computer 59 may display map 184 inthe same window as data output area 182 or as a distinct window.Analysis computer 59 may display random anomalies 198 and distinguishthem from detected repeated anomalies 200 in map 184. For example, inone embodiment, random anomalies 198 may appear in one color, such asblack, while repeated anomalies 200 may appear in a different color,such as red. In another embodiment, the number of occurrences of ananomaly in a certain position over a number of instances may dictate thecolor in which the anomalies are displayed in map 184. For example, map184 may display a composite map over the last 20 instances of datagathered for roller “4”. An anomaly occurring at a particular locationon the composite map only once may be displayed in black. An anomalyoccurring between 2 and 5 times in the same location on the compositemap may be displayed in green. An anomaly occurring between 6 and 10times may be displayed in yellow. An anomaly occurring 11 or more timesmay be displayed in red.

Slip area 183B displays information regarding whether rollers slip asthe web traverses the manufacturing system. This slip may be caused, forexample, when the web does not make constant contact with the roller.This may cause anomalies or defects to occur in the web when the webdoes come in contact with the roller. In any case, slip area 183Bdisplays a roll identifier column 194 and a priority column 196. Rollidentifier column 194 displays information that identifies the relevantroller. Priority column 196 indicates the priority, such as theseverity, of the roll slippage. Again, in other embodiments, other typesof priorities could be used, such as color-coded priorities or numericpriorities.

In one embodiment, analysis computer 59 may automatically sort the datadisplayed in data output area 182 based on priority, from highestpriority to lowest, based on the values in priority columns 188 and 196.In one embodiment, analysis computer 59 may automatically populate theuser interface, that is, without the need for a user to “refresh” thedata. In one embodiment, data output area 182 may display between 0 and20 entries. In one embodiment, data output area 182 may include a scrollbar, tabs, or other means by which to display a large number of entries.

Various embodiments of the invention have been described. These andother embodiments are within the scope of the following claims.

1. A method comprising: receiving roll synchronization signals from aplurality of sensors of a web manufacturing system, wherein each of thesensors corresponds to a different roller of the web manufacturingsystem, and wherein each of the roll synchronization signals indicatesthat the corresponding roller has completed a full rotation duringmanufacturing of a web of material; identifying positions of the rollsynchronization signals with respect to the web; receiving anomaly datafrom a web inspection system that identifies positions of anomalies onthe web; identifying a set of two or more of the anomalies as repeatedanomalies; identifying which of the rollers caused the repeatedanomalies by correlating the positions of the repeated anomalies withthe positions of the roll synchronization signals; and outputting anidentification of the identified one of the rollers.
 2. The method ofclaim 1, wherein identifying which of the rollers caused the repeatedanomalies comprises: for each of the rollers, (i) defining a pluralityof sequential segments in a downweb direction of the web for the roller,wherein the segments are defined to have an equal length in the down-webdirection, and wherein the length of the segments is defined to be equalto a distance corresponding to the completed rotations of the rollers asindicated by the roll synchronization for the roller; and (ii)determining that the rollers caused the repeated anomalies when therepeated anomalies occur at a substantially equal cross-web position anda substantially equal down-web distance from a beginning of at least athreshold number of the segments for the first one of the rollers. 3.The method of claim 1, wherein outputting further comprises outputtingcoordinates of the at least one of the anomalies, wherein thecoordinates include the cross-web position of the at least one of theanomalies and the down-web distance from the beginning of each of thesegments of the at least one of the anomalies.
 4. The method of claim 1,wherein identifying which of the rollers caused the repeated anomaliescomprises: determining a first anomaly position of a first one of theanomalies, a second anomaly position of a second one of the anomalies,and a third anomaly position of a third one of the anomalies; anddetermining that the first one of the anomalies, the second one of theanomalies and the third one of the anomalies are repeated anomaliescaused by the first one of the rollers when: (i) the first anomalyposition, the second anomaly position and the third anomaly positionhave a common cross-web position, (ii) are each separated in thedown-web direction by a distance greater than a predetermined minimumdistance defined by a circumference of a smallest one of the rollers,and (iii) the third anomaly occurs at a down-web position that is anmultiple of a down-web distance between the first anomaly position andthe second anomaly position.
 5. The method of claim 4, wherein the firstmultiple is one of one-third, one-half, one, two, or three.
 6. Themethod of claim 4, further comprising determining a fourth anomalyposition of a fourth one of the anomalies that has the same cross-webposition as that of the first anomaly position and that occurs at adown-web position that is a second multiple of the down-web distancebetween the first anomaly position and the second anomaly position. 7.The method of claim 1, wherein identifying which of the rollers causedthe repeated anomalies comprises: determining a first anomaly positionof a first one of the anomalies and a second anomaly position of asecond one of the anomalies; determining a down web distance between thefirst anomaly position and the second anomaly position; determining anexpected down web position of a completed rotation of the first one ofthe rollers by adding the determined down web distance to a position onthe web corresponding to a first completed rotation of the first one ofthe rollers; determining an error by subtracting the expected positionfrom an actual position for a completed rotation of a first one of therollers as determined from the roll synchronization signal for the firstone of the rollers; and determining that the first one of the rollerscaused the repeated anomalies when the error is below a predeterminedtolerance level.
 8. The method of claim 1, wherein determining that oneof the rollers caused at least one of the anomalies comprises: obtaininga circumference of the one of the rollers; creating a composite map thatrepresents a number of the segments of the web, wherein the compositemap has a length dimension proportionate to the length of the segmentsand a width dimension proportionate to the width of the segments;identifying positions of anomalies on the composite map in accordancewith positions of the anomalies on the segments; and determining thatthe one of the rollers caused at least one of the anomalies when asubset of the anomalies that includes the at least one of the anomaliesoccur at a certain position on the composite map a threshold number oftimes.
 9. The method of claim 8, wherein determining comprisesdetermining that the one of the rollers caused the at least one of theanomalies when the at least one of the anomalies occurs at the certainposition on the composite map for at least one-half of the number ofsegments.
 10. The method of claim 8, further comprising outputting thecomposite map such that positions of the anomalies are identified inaccordance with a number of occurrences of each of the anomalies at eachof the positions and an action for a user to take with respect to theone of the rollers.
 11. The method of claim 1, wherein identifying a setof two or more of the anomalies as repeated anomalies comprises:defining a plurality of sequential lanes in a crossweb direction of theweb; and separately processing inspection data for each of the lanes toidentify any repeated anomalies within each of the lanes.
 12. The methodof claim 1, wherein outputting an identification of the identified oneof the rollers comprises: associating each of the repeating anomalieswith the identified roller as causing the repeated anomaly; andoutputting each of the repeating anomalies and the associated rollercausing the repeated anomaly.
 13. The method of claim 1, whereinanomalies are potential or actual defects.
 14. A method comprising: fora web manufacturing system having a plurality of roller to transport aweb of material, storing data specifying for each of the rollers a sideof the web on which the roller is located; when transporting the web,receiving roll synchronization signals from a plurality of sensors,wherein each of the sensors corresponds to a different one of therollers, and wherein each of the roll synchronization signals indicatesthat the corresponding roller has completed a full rotation; identifyingpositions of the roll synchronization signals with respect to the web;receiving anomaly data from a web inspection system that identifiespositions of anomalies on the web; identifying a set of two or more ofthe anomalies as repeated anomalies; identifying which of the rollerscaused the repeated anomalies by correlating the positions of therepeated anomalies with the positions of the roll synchronizationsignals; determining on which side of the web the identified roller islocated; and outputting an data indicating the side of the web theidentified roller is located.
 15. A system comprising: a plurality ofrollers in contact with a web of material, wherein two or more of therollers each include a synchronization mark to indicate when thecorresponding roller has completed a full rotation; a plurality ofsynchronization mark readers that read the synchronization marks of theplurality of rollers and output roll synchronization signals, whereineach of the roll synchronization signals indicates that thecorresponding roller has completed a full rotation during manufacturingof the web; an encoder on at least one of the rollers that outputs aposition signal indicative of a down-web distance of the web; aninspection system that inspects the web and outputs anomaly dataidentifying positions of anomalies on the web; a synchronization unitthat receives the position signal from the encoder and the plurality ofroll synchronization signals from the synchronization mark readers,wherein the synchronization unit converts the occurrence of each of theroll synchronization signals into down-web positions within a coordinatesystem associated with web process line; and an analysis computer thatprocesses the anomaly data to identify a set of two or more of theanomalies as repeated anomalies, wherein the analysis computer outputsan indication of which of the rollers caused the repeated anomalies bycorrelating the positions of the repeated anomalies with the down-webpositions of the roll synchronization signals.
 16. The system of claim15, wherein the analysis computer identifies a first one of theanomalies at a first anomaly position, a second one of the anomalies ata second anomaly position, a third one of the anomalies at a thirdanomaly position, and a fourth one of the anomalies at a fourth anomalyposition, wherein each of the first anomaly position, the second anomalyposition, the third anomaly position, and the fourth anomaly positionoccur at a common cross-web position, wherein the first anomaly positionand the second anomaly position are separated by a first distance in thedown-web direction, the second anomaly position and the third anomalyposition are separated by a second distance in the down-web directionthat is a first multiple of the first distance, and the third anomalyposition and the fourth anomaly position are separated by a thirddistance in the down-web direction that is a second multiple of thefirst distance.
 17. The system of claim 16, wherein the first multipleand the second multiple are one of one-third, one-half, one, two, orthree.
 18. The system of claim 15, wherein the analysis computerdetermines a distance between two of the repeated anomalies, determinesa first rotation position on the web corresponding to a rotation of afirst one of the rollers, determines an estimated down-web rollerposition for a subsequent rotation of the first one of the rollers byadding the distance between the two repeated anomalies to the firstrotation position, determines an error by subtracting the estimateddown-web roller position for the first one of the rollers from an actualroller position determined by the synchronization unit, and determinesthat the first one of the rollers caused the repeated anomalies when theerror is below a predetermined tolerance level.
 19. The system of claim15, wherein, for each of the rollers having a synchronization mark, theanalysis computer: (i) defines a number of segments of the web such thateach of the segments has a length in the down-web direction defined bythe circumference of the roller, and (ii) forms a composite map that hasa length dimension proportionate to the length of the segments andidentifies positions of anomalies on the composite map in accordancewith positions of the anomalies within each of the segments, and whereinthe analysis computer determines which of the rollers caused therepeated anomalies when the at least one of the anomalies occurs in thecomposite map for that roller at least a threshold number of times. 20.The system of claim 19, wherein the threshold number of times is amajority of the number of the segments.
 21. The system of claim 19,further comprising a user interface that displays the composite map, arepresentation of a number of occurrences of each of the anomalies ofthe segments at each position, and a suggested action for a user to takewith respect to the one of the rollers.
 22. A computer-readable mediumcomprising instructions for causing a programmable processor to: receivesignals from sensors of a web manufacturing system, wherein each of thesensors corresponds to a roller of the web manufacturing system, andwherein each of the sensors sends a signal when the corresponding rollerhas completed a full rotation during manufacturing of a web of material;identify positions of the roll synchronization signals with respect tothe web; determine segments of the web, wherein a distance between thepositions of the roll synchronization signals corresponding to thecompleted rotations of each of the rollers defines the length of thesegments in the down-web direction; receive anomaly data from a webinspection system that identifies positions of anomalies on the web;identify corresponding positions of the anomalies on each of thesegments; determine that one of the rollers caused at least one of theanomalies when a subset of the anomalies that includes the at least oneof the anomalies repeatedly occur at a certain cross-web position and acertain down-web distance from the beginning of each of the segments fora threshold number of the segments; and output an identification of theone of the rollers.